##
## Call:
## lm(formula = mortality$`Log Concentration (micromolar)` ~ mortality$`Grp 1 Dead`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6365 -0.6935 -0.1116 0.9462 1.3852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.421424 0.640983 -2.218 0.0574 .
## mortality$`Grp 1 Dead` 0.004456 0.003378 1.319 0.2237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.098 on 8 degrees of freedom
## Multiple R-squared: 0.1786, Adjusted R-squared: 0.07596
## F-statistic: 1.74 on 1 and 8 DF, p-value: 0.2237
## 1 2 3 4 5 6
## -1.3144841 -1.3634981 -1.3011166 -0.6238326 -0.1069579 -0.2673673
## 7 8 9 10
## -0.5525395 -0.1114137 -0.7797861 -0.6862140
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.42 0.641 -2.22 0.0574
## 2 mortality$`Grp 1 Dead` 0.00446 0.00338 1.32 0.224
##
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~
## mortality$`Grp 1 Dead`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.176e-05 2.100e-08 2.100e-08 2.100e-08 6.247e-05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -68.278 39632.039 -0.002 0.999
## mortality$`Grp 1 Dead` 3.681 1956.504 0.002 0.998
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017e+00 on 9 degrees of freedom
## Residual deviance: 6.5811e-09 on 8 degrees of freedom
## AIC: 4
##
## Number of Fisher Scoring iterations: 25
## 1 2 3 4 5
## 1.000000e+00 1.339598e-09 1.000000e+00 1.000000e+00 1.000000e+00
## 6 7 8 9 10
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -68.3 39632. -0.00172 0.999
## 2 mortality$`Grp 1 Dead` 3.68 1957. 0.00188 0.998
## Dose SE
## p = 0.5: 18.55107 2919.175
## Concentration Mortality Fitted Predicted
## 1 0.00000 24 -1.3144841 1.000000e+00
## 2 -3.00000 13 -1.3634981 1.339598e-09
## 3 -2.00000 27 -1.3011166 1.000000e+00
## 4 -1.30103 179 -0.6238326 1.000000e+00
## 5 -1.00000 295 -0.1069579 1.000000e+00
## 6 -0.60206 259 -0.2673673 1.000000e+00
## 7 -0.30103 195 -0.5525395 1.000000e+00
## 8 0.00000 294 -0.1114137 1.000000e+00
## 9 0.39794 144 -0.7797861 1.000000e+00
## 10 0.69897 165 -0.6862140 1.000000e+00
##
## Call:
## lm(formula = mortality$`Concentration (micromolar)` ~ mortality$`Grp 1 Mortality Rate`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2022 -0.8268 -0.4406 -0.1633 3.8431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08094 0.98679 0.082 0.937
## mortality$`Grp 1 Mortality Rate` 4.81883 4.72512 1.020 0.338
##
## Residual standard error: 1.62 on 8 degrees of freedom
## Multiple R-squared: 0.115, Adjusted R-squared: 0.004431
## F-statistic: 1.04 on 1 and 8 DF, p-value: 0.3377
## 1 2 3 4 5 6 7
## 0.2119115 0.1511649 0.2127575 0.9039991 1.3022028 1.3610147 1.2453370
## 8 9 10
## 1.6692049 1.1965491 1.1568585
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.0809 0.987 0.0820 0.937
## 2 mortality$`Grp 1 Mortality Rate` 4.82 4.73 1.02 0.338
##
## Call:
## glm(formula = factor(mortality$`Concentration (micromolar)`) ~
## mortality$`Grp 1 Mortality Rate`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.54673 0.05413 0.07411 0.14427 0.96164
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1789 1.5701 0.114 0.909
## mortality$`Grp 1 Mortality Rate` 24.1835 33.6554 0.719 0.472
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 4.0868 on 8 degrees of freedom
## AIC: 8.0868
##
## Number of Fisher Scoring iterations: 8
## 1 2 3 4 5 6 7
## 0.6976561 0.6297867 0.6985508 0.9867371 0.9981813 0.9986455 0.9975821
## 8 9 10
## 0.9997112 0.9969134 0.9962357
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.179 1.57 0.114 0.909
## 2 mortality$`Grp 1 Mortality Rate` 24.2 33.7 0.719 0.472
## Dose SE
## p = 0.5: -0.00739562 0.07190727
## Concentration Mortality Fitted Predicted
## 1 0.000 0.02718007 0.2119115 0.6976561
## 2 0.001 0.01457399 0.1511649 0.6297867
## 3 0.010 0.02735562 0.2127575 0.6985508
## 4 0.050 0.17080153 0.9039991 0.9867371
## 5 0.100 0.25343643 1.3022028 0.9981813
## 6 0.250 0.26564103 1.3610147 0.9986455
## 7 0.500 0.24163569 1.2453370 0.9975821
## 8 1.000 0.32959641 1.6692049 0.9997112
## 9 2.500 0.23151125 1.1965491 0.9969134
## 10 5.000 0.22327470 1.1568585 0.9962357
##
## Call:
## lm(formula = mortality$`Concentration (micromolar)` ~ mortality$`Grp 2 Mortality Rate`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2004 -0.9545 -0.4889 -0.1804 3.8806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1899 1.0977 0.173 0.867
## mortality$`Grp 2 Mortality Rate` 3.5478 4.5533 0.779 0.458
##
## Residual standard error: 1.66 on 8 degrees of freedom
## Multiple R-squared: 0.07054, Adjusted R-squared: -0.04564
## F-statistic: 0.6071 on 1 and 8 DF, p-value: 0.4583
## 1 2 3 4 5 6 7
## 0.4912173 0.5207243 0.3658314 0.5366335 1.3003773 1.3493969 1.6007027
## 8 9 10
## 1.1219216 1.0047606 1.1194344
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.190 1.10 0.173 0.867
## 2 mortality$`Grp 2 Mortality Rate` 3.55 4.55 0.779 0.458
##
## Call:
## glm(formula = factor(mortality$`Concentration (micromolar)`) ~
## mortality$`Grp 2 Mortality Rate`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.73817 0.08303 0.14550 0.52556 0.93260
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3099 2.3857 -0.130 0.897
## mortality$`Grp 2 Mortality Rate` 18.4979 23.3103 0.794 0.427
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 4.8269 on 8 degrees of freedom
## AIC: 8.8269
##
## Number of Fisher Scoring iterations: 7
## 1 2 3 4 5 6 7
## 0.7792257 0.8045542 0.6473507 0.8172691 0.9958480 0.9967814 0.9991298
## 8 9 10
## 0.9895388 0.9808986 0.9894037
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -0.310 2.39 -0.130 0.897
## 2 mortality$`Grp 2 Mortality Rate` 18.5 23.3 0.794 0.427
## Concentration Mortality Fitted Predicted
## 1 0.000 0.08493353 0.4912173 0.7792257
## 2 0.001 0.09325044 0.5207243 0.8045542
## 3 0.010 0.04959197 0.3658314 0.6473507
## 4 0.050 0.09773463 0.5366335 0.8172691
## 5 0.100 0.31300514 1.3003773 0.9958480
## 6 0.250 0.32682194 1.3493969 0.9967814
## 7 0.500 0.39765555 1.6007027 0.9991298
## 8 1.000 0.26270524 1.1219216 0.9895388
## 9 2.500 0.22968198 1.0047606 0.9808986
## 10 5.000 0.26200418 1.1194344 0.9894037
##
## Call:
## lm(formula = mortality$`Concentration (micromolar)` ~ mortality$`Grp 3 Mortality Rate`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3889 -0.9068 -0.4670 0.1887 3.6424
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7662 1.4134 -0.542 0.603
## mortality$`Grp 3 Mortality Rate` 7.5246 5.8339 1.290 0.233
##
## Residual standard error: 1.567 on 8 degrees of freedom
## Multiple R-squared: 0.1722, Adjusted R-squared: 0.06867
## F-statistic: 1.664 on 1 and 8 DF, p-value: 0.2332
## 1 2 3 4 5 6
## 0.1543654 0.5734950 0.4619692 -0.2530723 1.1182804 1.3100746
## 7 8 9 10
## 1.8888659 1.4820422 1.3173576 1.3576219
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -0.766 1.41 -0.542 0.603
## 2 mortality$`Grp 3 Mortality Rate` 7.52 5.83 1.29 0.233
##
## Call:
## glm(formula = factor(mortality$`Concentration (micromolar)`) ~
## mortality$`Grp 3 Mortality Rate`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7073 0.1566 0.1850 0.3979 1.1001
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.085 2.673 -0.406 0.685
## mortality$`Grp 3 Mortality Rate` 18.610 17.614 1.057 0.291
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 4.7800 on 8 degrees of freedom
## AIC: 8.78
##
## Number of Fisher Scoring iterations: 6
## 1 2 3 4 5 6 7
## 0.7671541 0.9028179 0.8757843 0.5460206 0.9727835 0.9828877 0.9958571
## 8 9 10
## 0.9887494 0.9831881 0.9847573
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.08 2.67 -0.406 0.685
## 2 mortality$`Grp 3 Mortality Rate` 18.6 17.6 1.06 0.291
## Concentration Mortality Fitted Predicted
## 1 0.000 0.12234043 0.1543654 0.7671541
## 2 0.001 0.17804154 0.5734950 0.9028179
## 3 0.010 0.16322009 0.4619692 0.8757843
## 4 0.050 0.06819313 -0.2530723 0.5460206
## 5 0.100 0.25044196 1.1182804 0.9727835
## 6 0.250 0.27593085 1.3100746 0.9828877
## 7 0.500 0.35285054 1.8888659 0.9958571
## 8 1.000 0.29878485 1.4820422 0.9887494
## 9 2.500 0.27689873 1.3173576 0.9831881
## 10 5.000 0.28224974 1.3576219 0.9847573
##
## Call:
## lm(formula = mortality$`Concentration (micromolar)` ~ mortality$`Grp 4 Mortality Rate`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4074 -0.7780 -0.0310 0.1035 3.4778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3737 0.8647 -0.432 0.677
## mortality$`Grp 4 Mortality Rate` 6.4602 3.5981 1.795 0.110
##
## Residual standard error: 1.454 on 8 degrees of freedom
## Multiple R-squared: 0.2872, Adjusted R-squared: 0.1981
## F-statistic: 3.224 on 1 and 8 DF, p-value: 0.1103
## 1 2 3 4 5 6
## -0.10359993 0.03878443 -0.09334194 0.07413448 1.42240778 1.65739176
## 7 8 9 10
## 1.47353925 1.19128665 2.22816553 1.52223198
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -0.374 0.865 -0.432 0.677
## 2 mortality$`Grp 4 Mortality Rate` 6.46 3.60 1.80 0.110
##
## Call:
## glm(formula = factor(mortality$`Concentration (micromolar)`) ~
## mortality$`Grp 4 Mortality Rate`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.454e-04 2.100e-08 2.100e-08 2.100e-08 2.500e-04
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -928.5 131515.6 -0.007 0.994
## mortality$`Grp 4 Mortality Rate` 21790.2 3084822.2 0.007 0.994
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017e+00 on 9 degrees of freedom
## Residual deviance: 1.2270e-07 on 8 degrees of freedom
## AIC: 4
##
## Number of Fisher Scoring iterations: 25
## 1 2 3 4 5
## 3.009981e-08 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## 6 7 8 9 10
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -929. 131516. -0.00706 0.994
## 2 mortality$`Grp 4 Mortality Rate` 21790. 3084822. 0.00706 0.994
## Concentration Mortality Fitted Predicted
## 1 0.000 0.04181687 -0.10359993 3.009981e-08
## 2 0.001 0.06385696 0.03878443 1.000000e+00
## 3 0.010 0.04340473 -0.09334194 1.000000e+00
## 4 0.050 0.06932890 0.07413448 1.000000e+00
## 5 0.100 0.27803204 1.42240778 1.000000e+00
## 6 0.250 0.31440589 1.65739176 1.000000e+00
## 7 0.500 0.28594683 1.47353925 1.000000e+00
## 8 1.000 0.24225613 1.19128665 1.000000e+00
## 9 2.500 0.40275762 2.22816553 1.000000e+00
## 10 5.000 0.29348411 1.52223198 1.000000e+00
##
## Call:
## lm(formula = mortality$`Concentration (micromolar)` ~ mortality$`Grp 5 Mortality Rate`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1278 -0.8809 -0.4994 -0.0615 4.0337
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2286 1.1204 0.204 0.843
## mortality$`Grp 5 Mortality Rate` 3.5148 4.8755 0.721 0.491
##
## Residual standard error: 1.669 on 8 degrees of freedom
## Multiple R-squared: 0.061, Adjusted R-squared: -0.05637
## F-statistic: 0.5197 on 1 and 8 DF, p-value: 0.4915
## 1 2 3 4 5 6 7
## 0.8609697 0.3262437 0.4443543 0.6143513 0.9875522 1.3777772 1.5649239
## 8 9 10
## 0.9736144 1.2949091 0.9663041
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.229 1.12 0.204 0.843
## 2 mortality$`Grp 5 Mortality Rate` 3.51 4.88 0.721 0.491
##
## Call:
## glm(formula = factor(mortality$`Concentration (micromolar)`) ~
## mortality$`Grp 5 Mortality Rate`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1344 0.4017 0.4490 0.4876 0.5433
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.778 2.084 0.853 0.393
## mortality$`Grp 5 Mortality Rate` 2.175 9.842 0.221 0.825
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 6.4522 on 8 degrees of freedom
## AIC: 10.452
##
## Number of Fisher Scoring iterations: 5
## 1 2 3 4 5 6 7
## 0.8974986 0.8628044 0.8712304 0.8825805 0.9044848 0.9234094 0.9312101
## 8 9 10
## 0.9037369 0.9197024 0.9033426
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 1.78 2.08 0.853 0.393
## 2 mortality$`Grp 5 Mortality Rate` 2.18 9.84 0.221 0.825
## Concentration Mortality Fitted Predicted
## 1 0.000 0.17993080 0.8609697 0.8974986
## 2 0.001 0.02779456 0.3262437 0.8628044
## 3 0.010 0.06139852 0.4443543 0.8712304
## 4 0.050 0.10976479 0.6143513 0.8825805
## 5 0.100 0.21594509 0.9875522 0.9044848
## 6 0.250 0.32696897 1.3777772 0.9234094
## 7 0.500 0.38021454 1.5649239 0.9312101
## 8 1.000 0.21197961 0.9736144 0.9037369
## 9 2.500 0.30339196 1.2949091 0.9197024
## 10 5.000 0.20989975 0.9663041 0.9033426
##
## Call:
## lm(formula = mortality$`Concentration (micromolar)` ~ mortality$`Grp 6 Mortality Rate`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2447 -0.6833 -0.5704 -0.1608 4.0172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4249 0.9138 0.465 0.654
## mortality$`Grp 6 Mortality Rate` 2.6164 3.7769 0.693 0.508
##
## Residual standard error: 1.672 on 8 degrees of freedom
## Multiple R-squared: 0.05659, Adjusted R-squared: -0.06134
## F-statistic: 0.4799 on 1 and 8 DF, p-value: 0.5081
## 1 2 3 4 5 6 7
## 0.5214304 0.6203105 0.4962614 0.6883745 0.7983044 1.4946779 1.5760699
## 8 9 10
## 1.0523016 1.1805079 0.9827616
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.425 0.914 0.465 0.654
## 2 mortality$`Grp 6 Mortality Rate` 2.62 3.78 0.693 0.508
##
## Call:
## glm(formula = factor(mortality$`Concentration (micromolar)`) ~
## mortality$`Grp 6 Mortality Rate`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.38135 0.00049 0.00902 0.18003 1.18904
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.432 2.894 -0.495 0.621
## mortality$`Grp 6 Mortality Rate` 51.454 63.577 0.809 0.418
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 3.5456 on 8 degrees of freedom
## AIC: 7.5456
##
## Number of Fisher Scoring iterations: 9
## 1 2 3 4 5 6 7
## 0.6148266 0.9177540 0.4931693 0.9770396 0.9973022 1.0000000 1.0000000
## 8 9 10
## 0.9999817 0.9999985 0.9999281
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.43 2.89 -0.495 0.621
## 2 mortality$`Grp 6 Mortality Rate` 51.5 63.6 0.809 0.418
## Concentration Mortality Fitted Predicted
## 1 0.000 0.03691275 0.5214304 0.6148266
## 2 0.001 0.07470511 0.6203105 0.9177540
## 3 0.010 0.02729306 0.4962614 0.4931693
## 4 0.050 0.10071942 0.6883745 0.9770396
## 5 0.100 0.14273504 0.7983044 0.9973022
## 6 0.250 0.40889167 1.4946779 1.0000000
## 7 0.500 0.44000000 1.5760699 1.0000000
## 8 1.000 0.23981374 1.0523016 0.9999817
## 9 2.500 0.28881469 1.1805079 0.9999985
## 10 5.000 0.21323529 0.9827616 0.9999281
